利用机器学习预测重症监护室压疮的发病率

E. Cramer, Martin G. Seneviratne, H. Sharifi, Alp Ozturk, T. Hernandez-Boussard
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引用次数: 50

摘要

背景:在重症监护室(ICU)减少医院获得性压疮(PU)已成为国际卫生系统的一项重要质量指标。使用电子健康记录(EHR)的观察数据来描述ICU中PU的特征的工作有限。因此,用于确定患者PU发展风险的基于EHR的预后工具有限,大多数机构依赖护士计算的风险评分,如Braden评分来识别高危患者。方法和结果:使用来自50851名三级ICU(MIMIC-III)患者的EHR数据,我们发现2期或以上PUs的患病率为7.8%。对于1690例在第2天或之后记录PU的入院患者,我们评估了在前24小时内测量的Braden评分的预后价值。高危Braden评分(<=12)对PU未来发展的准确度为0.09,召回率为0.50。我们在最初的24小时内使用EHR提供的人口统计参数、诊断代码、实验室值和生命体征训练了一系列机器学习算法。加权线性回归模型显示,未来PU开发的精确度为0.09,召回率为0.71。将Braden分数元素集成到模型中并没有提高分类器的性能。结论:我们证明了基于EHR的模型作为PU的筛查工具可以优于Braden评分。这可能是在入院早期自动进行风险分层的有用工具,有助于指导ICU的质量协议,包括预防性干预的分配和时间安排。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning
Background: Reducing hospital-acquired pressure ulcers (PUs) in intensive care units (ICUs) has emerged as an important quality metric for health systems internationally. Limited work has been done to characterize the profile of PUs in the ICU using observational data from the electronic health record (EHR). Consequently, there are limited EHR-based prognostic tools for determining a patient’s risk of PU development, with most institutions relying on nurse-calculated risk scores such as the Braden score to identify high-risk patients. Methods and Results: Using EHR data from 50,851 admissions in a tertiary ICU (MIMIC-III), we show that the prevalence of PUs at stage 2 or above is 7.8 percent. For the 1,690 admissions where a PU was recorded on day 2 or beyond, we evaluated the prognostic value of the Braden score measured within the first 24 hours. A high-risk Braden score (<=12) had precision 0.09 and recall 0.50 for the future development of a PU. We trained a range of machine learning algorithms using demographic parameters, diagnosis codes, laboratory values and vitals available from the EHR within the first 24 hours. A weighted linear regression model showed precision 0.09 and recall 0.71 for future PU development. Classifier performance was not improved by integrating Braden score elements into the model. Conclusion: We demonstrate that an EHR-based model can outperform the Braden score as a screening tool for PUs. This may be a useful tool for automatic risk stratification early in an admission, helping to guide quality protocols in the ICU, including the allocation and timing of prophylactic interventions.
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